Lastly, let us consider synthetic-a-posteriori-based creative knowledge. This category includes pieces of knowledge that emerge owing to pragmatic individual preferences, which are led by data- or experience-driven activities. For example, consider a signal (time series) that provides information pertaining to surface heights of an arbitrary machined surface, as depicted in Figure 8(a ). Some pieces of knowledge underlying the signal are given by the concept map, depicted in Figure 8(b ), which boils down to following statements—(1) A signal (time series) may comprise three stochastic features—trends, noises, and bursts; (2) The stochastic features can be defined using functions T , N , and B , respectively; (3) Functions T , N , and B can be added to yield function S given by S = T + N+ B ; and (4) S can be used to simulate signals (time series). The first statement is an example of synthetic-a-posteriori-based creative knowledge, because it seems (to an individual) that the given signal (Figure 8(a )) is caused by the stochastic features (trends, noises, and bursts)Other individuals might imagine it differently. The second and third statements represent pieces of definitional knowledge, whereas the last statement qualifies as analytic-a-priori-based creative knowledge, since it is yet unclear if signals can be accurately simulated by the function S .